Gnss-receiver interference detection using deep learning

ABSTRACT

Systems and methods are described for classification of interference for GNSS receivers. One or more neural networks are utilized to classify RF signal data received by a GNSS receiver. The classification associates the RF signal data with an RF environment. Appropriate interference mitigation techniques can be implemented by the receiver based on the classification.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of commonly assigned copendingU.S. Pat. Application Serial No. 16/860,536, which was filed on Apr. 28,2020, by Robert Hang, et al., for GNSS-RECEIVER INTERFERENCE DETECTIONUSING DEEP LEARNING, which is hereby incorporated by reference.

BACKGROUND Technical Field

The present disclosure generally relates to mitigation of radiofrequency (RF) interference in global satellite system (GNSS) receivers.The present disclosure more particularly relates to using neuralnetworks for classification of radio frequency (RF) interferenceenvironments experienced by GNSS receivers.

Description of Related Art

A fundamental and crucial step in dealing with radio frequency (RF)interference experienced by a global navigation satellite system (GNSS)receiver is the ability to detect interference in a consistent andaccurate way. If interference is reported by the detector when there isno actual interference, the receiver would attempt to estimate theinterference parameters of non-existent interference and would apply amitigation method when none is needed thereby causing unnecessaryperformance degradation. Conversely, if interference is present but itis not detected, the receiver will not apply any mitigation method andwill operate in a degraded fashion or simply not operate at all,depending on the severity of the interference.

Prior techniques of addressing GNSS receiver interference have utilizedtwo algorithms for interference detection: spectral analysis andstatistical analysis. Spectral analysis can be well suited for detectionof in-band (IB) interference, i.e., interference which frequencycomponents are within the passband of the RF section (“deck”), whilestatistical analysis can be well suited for detection of out-of-band(OOB) interference, i.e., interference which frequency components areoutside the RF deck passband. Variants and refinements of these methodscan be used-albeit with not inconsiderable effort and individualizedalgorithmic construction-to correctly detect interference corner cases.

A significant drawback of such approaches is the two detectionalgorithms (spectral and statistical analysis) must be concurrentlyrunning, possibly along with their variants, leading to complexity interms of operating them and making sure that their concurrent use doesnot result in conflicting detection outcomes. These algorithms are oftenreferred to as rule-based methods because they follow explicit stepsdescribed by the algorithms to achieve the desired detectionperformance.

Using such prior techniques, when a new type of interference is to berecognized at the GNSS receiver, that type of interference must first becharacterized and then a new detector (e.g., a variant of the spectralor statistical algorithms) would need to be devised specifically forthat newly characterized interference. This new detector would then needto be operated in addition to the previous two algorithms and theirvariants/refinements. Overall, this makes the detection more complex,harder to maintain, and more complex to ensure correct performance, notto mention the significant work involved in devising the new detectionalgorithm.

SUMMARY

An aspect of the present disclosure is directed to classification ofinterference for GNSS receivers, e.g., GPS, GLONASS, and the like. Oneor more neural networks are utilized to classify RF signal data receivedby a GNSS receiver. The classification associates the RF signal datawith an RF environment. Appropriate interference mitigation techniquescan be implemented by the receiver based on the classification.

An aspect of the present disclosure is directed to a GNSS processorarchitecture for processing GNSS receiver signal data, the processingarchitecture including: a processor; and a memory unit in communicationwith the processor via a communication infrastructure (e.g., bus) andstoring processor-readable instructions; wherein, when executed by theprocessor, the processor-readable instructions cause the processor to:receive RF signal data associated with a class of RF environment;provide the RF signal data to a neural network for classification of theRF signal data as belonging to a pre-defined type of RF environment;using the neural network, classify the RF signal data as belonging to apre-defined type of RF environment; and apply an interference mitigationtechnique corresponding to the type of RF environment that has beenclassified.

For the GNSS processor architecture, the neural network can include orbe composed of an artificial neural network (ANN). For the GNSSprocessor architecture, the neural network can include or be composed ofa convolutional neural network (CNN). For the GNSS processorarchitecture, the RF signal data can be classified as belonging to oneof three RF environments, no interference, in-band interference, andout-of-band interference. For the GNSS processor architecture, theneural network can include a first layer used to process input images,e.g., power spectral density (PSD) images. Such PSD images may be (butare not necessarily) configured in a 128 x 128 pixels format. For theGNSS processor architecture, when the neural network includes a CNN, theCNN can include eight convolutional layers configured to process the PSDimages.

When a CNN is used for the GNSS processor architecture, a 6 x 6 kernelcan be used for feature extraction using max pooling. When a CNN is usedfor the GNSS processor architecture, a last convolution layer can beconnected to a fully-connected layer with 32 neurons. For the GNSSprocessor architecture, the neural network can include an output layerhaving three (3) neurons configured to output classificationdeterminations of one of three interference environments, using a softmax activation function. For the GNSS processor architecture, the neuralnetwork can include or be composed of a recurrent neural network (RNN).Such a RNN can include or be composed of a Long Short-Term Memory (LSTM)RNN. For the GNSS processor architecture, the neural network can includeor be composed of an autoencoder. For the GNSS processor architecture,the neural network can include or be composed of a Restricted BoltzmannMachine (RBM).

These, as well as other components, steps, features, objects, benefits,and advantages, will now become clear from a review of the followingdetailed description of illustrative embodiments, the accompanyingdrawings, and the claims.

BRIEF DESCRIPTION OF DRAWINGS

The drawings are of illustrative embodiments. They do not illustrate allembodiments. Other embodiments may be used in addition or instead.Details that may be apparent or unnecessary may be omitted to save spaceor for more effective illustration. Some embodiments maybe practicedwith additional components or steps and/or without all of the componentsor steps that are illustrated. When the same numeral appears indifferent drawings, it refers to the same or like components or steps.

FIG. 1 depicts a diagram of an example of a convolutional neural network(CNN) architecture embodiment of the present disclosure.

FIG. 2 depicts a diagram of an implemented interference classificationand verification system in accordance with the present disclosure.

FIG. 3 depicts a diagram of an example of an artificial neural network(ANN) architecture embodiment of the present disclosure.

FIG. 4 depicts a block diagram of an example of a method of detectingand classifying interference in accordance with the present disclosure.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

Illustrative embodiments are now described. Other embodiments may beused in addition or instead. Details that may be apparent or unnecessarymay be omitted to save space or for a more effective presentation. Someembodiments may be practiced with additional components or steps and/orwithout all of the components or steps that are described.

An aspect of the present disclosure is directed to distinguishingdifferent types of RF environments seen by a GNSS receiver as differentclasses of interference, and then using a neural network to classifyinterference at the receiver into one or more of these RF environmentclasses (and thereby detect interference at the receiver). For example,an interference-free RF environment can be considered a class to bedetected just like an RF environment with interference. Interferenceenvironments can be further classified into in-band (IB) of out-of-band(OOB) interference. Some classes can then be split to create newclasses, e.g., weak IB and strong IB interference. Once all types ofinterference—or, interference environments—have been identified, theneural network can be used to classify them when experienced by a GNSSreceiver. Such a neural network is trained and validated using the dataassociated with defined or pre-defined classes of RF interferenceexperienced by a GNSS receiver.

The data that are associated with the interference classes can be of anyreceiver metric that well characterizes the classes, e.g., PSD,carrier-to-noise ratio (C/No), pseudo-noise tracking error, estimatedpositioning error, etc. Once trained to the required performance level,the neural network is used in a prediction mode where receiver inputmetrics can then be processed, with the neural network outputtingresults for each of the classes. The class with the highest outputmetric is then selected (classified) as the detected RF environment. Inthe situation where a new type of interference needs to be supported,this new type of interference is characterized (e.g., PSD with that newtype of interference), the associated data is created, and then theneural network is re-trained/re-tuned against that new data and the dataof all previously known interference types. This is in contrast withprior rule-based algorithms for which characterization of the newinterference requires creation of a new rule, i.e., creation of a newdetector algorithm, representing a significant undertaking.

An aspect of the present disclosure is directed to using artificialneural networks (which may variously be referred to as “ANNs,” “NNs,” or“neural networks”) for the classification and detection of interferencein or by GNSS receivers. A neural network, or group of neural networks,is utilized as a detector that is capable of detecting any type ofinterference, instead of relying on several rule-based algorithms (e.g.,spectral analysis, statistical analysis, and their variants). A neuralnetwork can perform interference detection by classification based onone or more characteristics. For example, a neural network can classifya type of interference from power spectral densities (PSD) valuesdirectly. Suitable types of neural networks that may be used within thescope of the present disclosure include, but are not limited to, ANNs,convolutional neural networks (“CNNs”), and recurrent neural networks(“RNNs”), among others. FIG. 1 depicts a diagram of an example of an ANNarchitecture 100 embodiment of the present disclosure. As shown, ANN 100includes a number of input images, e.g., power spectral densities (PSDs)of GNSS signals received at a receiver. The pixel values of one or moreinput images are provided to an input layer of a fully-connected sectionthat also has an output layer and a number (N) of hidden layers.

In exemplary embodiments, convolutional neural networks (CNNs) can beused as detector. Working as image classifiers, CNNs can use PSDs asimages. By virtue of processing images, CNNs have the advantage thatthey can readily be used for visual aid such as highlighting the zone ofthe spectrum where interference is located. FIG. 1 depicts a diagram ofan example of a CNN architecture 200 in accordance with the presentdisclosure.

As shown in FIG. 1 , CNN 100 includes an input image 110, e.g., a PSD(as shown), a convolutional and pooling section 120 with a number ofconvolutional and pooling layers 120(1-M), a fully-connected section 130with a number of fully connected layers 130 (1-N), and an output 140section. The output section includes a number of nodes, e.g., three, forclassification. In exemplary embodiments, a Softmax function maybe usedfor the classification. The convolutional and pooling section 120 caninclude one or more convolutional layers and one or more pooling layers.A convolutional layer can be cascaded with another convolutional layeror a pooling layer. Similarly, a pooling layer can be cascaded with aconvolutional layer or a pooling layer or maybe connected to afully-connected layer, e.g., of the fully-connected section 130.

In operation of CNN 100, the convolutional and pooling section 120implements a convolutional 2D filter (kernel) for feature extraction ofan input, e.g., input PSD image. Max pooling reduces the spatial size ofthe convolved feature(s). After the convolution and pooling operationsare performed by the convolution and pooling section, thefully-connected section 130 is used to learn non-linear combinations ofthe features (e.g., high-level features) that are represented by theoutput of the convolution and pooling section 120. A flattening layercan be used for some applications for the fully-connected section 130.The output section 140 receives the output of the fully-connectedsection 130 and then classifies the input images, e.g., PSDs,accordingly. As a preliminary step, one or more appropriate data setscan be utilized for training and validation purposes for the CNN 100.

The CNN 100 may have any type of suitable CNN configuration, e.g.,LeNet, AlexNet, VGG, VGGNet, GoogLeNet, ResNet, ZFNet, XCeption CNN,Inception v3 or v4, or the like. In some applications, a dilatedconvolution 2D filter may be used, e.g., to reduce computation costs oraccommodate a certain computation performance level. In someapplications, valid padding or same padding may be used for theconvolution layer(s), e.g., when it is desired to increase or keep samethe dimensionality of an input image. In exemplary embodiments,different types of neural networks can be combined, e.g., T cascaded,for detection. For example, an ANN can be followed by a CNN if somerefinements are needed or desired, etc.

FIG. 2 depicts a diagram of an implemented interference classificationand verification system 200 in accordance with the present disclosure.The system 200 includes a GNSS receiver 210, connected to an antenna 220and a personal computer (PC) 230. A signal generator 222 was used to addvarious noise and signal profiles to simulate different RF environmentsfor classification. The prototype embodiment 200 was implemented using amain Python script 232 resident on PC 230. The PC 230 also implementedNovAtel Connect™ as a graphic user interface (GUI). NovAtel Connect ™ isa windows-based GUI that allows a user to access a NovAtel receiver’smany features without the need to use a terminal emulator or to writespecial software and allows the user to easily communicate and configurethe receiver via serial port, USB or ethernet connection using a PCrunning the Windows 7 or Windows 10 operating system. The Python script232 consisted of a block configured for retrieving power spectraldensity (PSD) logs from a receiver (NovAtel OEM7 receiver) andperforming detection of three classes of radio-frequency (RF)environments: no interference, in-band (IB) interference and out-of-band(OOB) interference all in real-time.

For the implemented prototype embodiment, a first layer was used toinput power spectral density (PSD) images, which were in a 128 × 128pixels format. Eight (8) convolutional layers then were used to processthe images, using a 6 × 6 kernel (window filter) for feature extractionusing max pooling. The last convolution layer then was connected to afully-connected layer with 32 neurons, which used the ReLU activationfunction. An output layer of three (3) neurons then was used to outputclassification determinations of one of three interference environments,using a soft max activation function.

For training purposes, 26500 PSD images were used; 14000 were used forvalidation. The PSD images were labeled with the following labels: (i)type of interference, (ii) frequency, (iii) power level, and (iv)bandwidth of interference. An accuracy of detection of 98.80% wasobtained during the off-line validation of the deep learning-basedinterference detector against captured PSD logs.

FIG. 3 depicts a diagram of an example of an ANN architecture 300 inaccordance with an alternate embodiment of the present disclosure. Asshown, ANN 300 receives and processes a number of input images 310(1-N),e.g., power spectral densities (PSDs) of GNSS signals received at or bya GNSS receiver. ANN 300 includes a fully-connected section 320 having anumber (M) of fully-connected layers in a deep learning configuration.The fully-connected section 320 includes a number (M-2) of hidden layersand provides an output classification 330.

In operation, one or more input images, e.g., PSDs, are provided to theinput layer of the fully-connected section 320 and then are processed bythe number (M-2) of hidden layers. The last layer of the fully-connectedsection 320 performs classification of the input images, e.g., by usinga Softmax activation function, determining which class the input imageis most likely to be associated with.

FIG. 4 depicts a block diagram of an example of an interferencedetection and classification method 400, in accordance with the presentdisclosure. For method 400, RF signal data can be received by areceiver, with the RF signal data being associated with a type or classof RF environment, as shown at 402. Non-limiting examples of such typesor classes of RF environments include but are not limited to (i)interference-free, (ii) in-band (IB) interference, and (iii) out-of-band(OOB) interference. The RF signal data can be provided to a neuralnetwork, e.g., CNN 100 of FIG. 1 , for classification of the data asbelonging to a pre-defined type of RF environment, as shown at 404.

Continuing with the description of method 400, using the neural network,the type of RF environment of or associated with the RF signal data canbe classified, as shown at 406. One or more mitigation techniques, e.g.,application of a particular filter, can then be applied to the RF signaldata or subsequent data based on or corresponding to the classificationof the RF environment, as shown at 408. Exemplary mitigation techniquesinclude, but are not limited to, implementing suitable filters, and/orapplying controlled radiation pattern antenna (e.g., null-forming) forany type of interference but more so for interference that are at thesame frequency as GNSS signals . Hyperparameters of the neural networkcan be tuned or optimized-at the training stage-for improved accuracyand/or computational efficiency, as shown at 410.

One of skill in the art will understand that each block or step shownand described for FIG. 4 can be implemented in suitable code ascomputer-readable instructions resident or stored in suitable memory,e.g., within GNSS receiver 310 of FIG. 3 . The instructions can beimplemented by a suitable processor, e.g., resident in GNSS receiver310, that is/are connected to the memory, and when executed by theprocessor cause the processor to perform the functions shown anddescribed for FIG. 4 .

As noted above, hyperparameter tuning (hyperparameter optimization) ofthe deep-learning based detector may be employed to lead to higherdetection accuracy. For a CNN, any suitably-sized filters or kernels(e.g., 2-by-2, 3-by-2, 3-by-3, 4-by-2, 4-by-3, 4-by-4, etc.) may be usedfor striding (kernel size). Any suitable pooling technique can be used,e.g., max pooling or average pooling can be used. Max pooling was usedfor the implemented embodiment and may be preferable for noisefiltering. Any suitable cost (loss) function can be used, e.g.,quadratic cost (a/k/a, mean squared error or MSE, maximum likelihood, orsum-squared error), cross-entropy cost, exponential cost, Hellingerdistance, Kullback-Leibler divergence, etc.

Any suitable activation function can be used, e.g., rectified linearunit (ReLU), Softmax, etc. In exemplary embodiments, ReLU is used. Anysuitable optimization (solver) algorithm can be used for a neuralnetwork. For example, various types of gradient descent algorithms canbe used. In exemplary embodiments, a stochastic gradient descent (SGD)algorithm can be used. Hyperparameters that can be optimized in SGDinclude learning rate, momentum, decay, and Nesterov (which takes thevalue true or false depending on whether one wants to apply Nesterovmomentum). Alternatively, or in conjunction, a root mean squaredoptimizer such as a root mean squared propagation (RMSProp) optimizercan be used. For some applications, an adaptive moment estimation (Adam)optimizer can be used.

Unless otherwise indicated, the neural networks and classificationmethods that have been discussed herein are implemented with aspecially-configured computer system, e.g., a GNSS receiver,specifically configured to perform the functions that have beendescribed herein for the component. Each computer system includes one ormore processors, tangible memories (e.g., random access memories (RAMs),read-only memories (ROMs), and/or programmable read only memories(PROMS)), tangible storage devices (e.g., hard disk drives, CD/DVDdrives, and/or flash memories), system buses, video processingcomponents, network communication components, input/output ports, and/oruser interface devices (e.g., keyboards, pointing devices, displays,microphones, sound reproduction systems, and/or touch screens).

Each computer system for the neural network(s) and classificationmethod(s) may be or include a desktop computer or a portable computer,such as a laptop computer, a notebook computer, a tablet computer, aPDA, a smartphone, or part of a larger system, such a vehicle,appliance, and/or telephone system. Each computer system for the neuralnetwork(s)and classification may include one or more computers at thesame or different locations. When at different locations, the computersmay be configured to communicate with one another through a wired and/orwireless network communication system.

Each computer system may include software (e.g., one or more operatingsystems, device drivers, application programs, and/or communicationprograms). When software is included, the software includes programminginstructions and may include associated data and libraries. Whenincluded, the programming instructions are configured to implement oneor more algorithms that implement one or more of the functions of thecomputer system, as recited herein. The description of each functionthat is performed by each computer system also constitutes a descriptionof the algorithm(s) that performs that function.

The software may be stored on or in one or more non-transitory, tangiblestorage devices, such as one or more hard disk drives, CDs, DVDs, and/orflash memories. The software may be in source code and/or object codeformat. Associated data may be stored in any type of volatile and/ornon-volatile memory. The software may be loaded into a non-transitorymemory and executed by one or more processors.

The components, steps, features, objects, benefits, and advantages thathave been discussed are merely illustrative. None of them, nor thediscussions relating to them, are intended to limit the scope ofprotection in any way. Numerous other embodiments are also contemplated.These include embodiments that have fewer, additional, and/or differentcomponents, steps, features, objects, benefits, and/or advantages. Thesealso include embodiments in which the components and/or steps arearranged and/or ordered differently.

For example, while the above description has been provided in thecontext of using CNNs and ANNs, other types of neural networks such asrecurrent neural networks (RNNs) and Restricted Boltzmann Machines(RBMs) can be used within the scope of the present disclosure, as oneskilled in the art will appreciate.

The Long Short-Term Memory (LSTM) variant or type of Recurrent NeuralNetwork (RNN) can be used in the context of the present disclosure. AnLSTM unit in RNN is composed of four main elements; the memory cell andthree logistic gates (Read, Write and Forget gates). Manipulating thesegates gives the RNN the ability to remember what it needs and forgetswhat is no longer useful for predicting a sequence. By stacking layersto create hierarchical feature representation of the input data whichthen feeds as input to the second layer, the next LSTM will blend thenew input into its own internal state to produce an output. StackingLSTM hidden layers will make the model deeper and allow for greatermodel complexity and probably leading to more accurate result.

RNNs can be used to detect certain types of time-varying interferencesuch as sweeping interference in which frequency center changes overtime i.e. successive PDS images show interference at differentfrequencies. Such sequence of frequencies could be detected by an RNN asits memory elements makes it suitable to store past information aboutPDSs that follow a certain pattern.

Another aspect of the present disclosure includes the use ofunsupervised learning algorithms including Restricted Boltzmann Machines(RBMs) and autoencoders.

RBMs work well with unlabeled data such as images, videos and audiofiles. The weights of the neural network are adjusted in such a way thatthe RBM can extract the relationships among the input features and thendetermines which features are relevant to achieve the expected results.After training, the network will have the ability to reconstruct theinput image. RBMs learn from data and “autoencode” their own structurein a stochastic way for more efficient dimensionality reduction whichmake them work better than the popular Autoencoders or the PrincipalComponent Analysis (PCA) methods.

Once detection has been performed, e.g., by an RBM, another NN can beused to determine more parameters of the interference source such asfrequency center, bandwidth, power, etc. For example, once aninterference source has been detected (i.e., image classified) as inband (IB) by the detection NN, the estimation NN (e.g., provided by anNN used for regression) can be used to localize the interference withinthe image which means that the NN can estimate parameters such as centerfrequency, bandwidth, power level, etc. Most of these parameters arediscrete (e.g., center frequency can take any values between 1550 MHz to1590 MHz ) so they do not lend themselves to being grouped into classesfor detection hence the use of an regression NN (called estimation NN)for performing estimation after detection has been declared by thedetection NN. Another reason for using two separate NNs, a detection NNand an estimation NN, is because for some classes such as strongout-of-band interference (OOB), PSDs are distorted in such way that itis not possible to estimate parameters such as center frequency andbandwidth. In that situation the estimation NN should not attempt toperform estimation if the detection NN detects such class.

Unless otherwise stated, all measurements, values, ratings, positions,magnitudes, sizes, and other specifications that are set forth in thisspecification, including in the claims that follow, are approximate, notexact. They are intended to have a reasonable range that is consistentwith the functions to which they relate and with what is customary inthe art to which they pertain. The phrase “means for” when used in aclaim is intended to and should be interpreted to embrace thecorresponding structures and materials that have been described andtheir equivalents. Similarly, the phrase “step for” when used in a claimis intended to and should be interpreted to embrace the correspondingacts that have been described and their equivalents. The absence ofthese phrases from a claim means that the claim is not intended to andshould not be interpreted to be limited to these correspondingstructures, materials, or acts, or to their equivalents.

The scope of protection is limited solely by the claims that now follow.That scope is intended and should be interpreted to be as broad as isconsistent with the ordinary meaning of the language that is used in theclaims when interpreted in light of this specification and theprosecution history that follows, except where specific meanings havebeen set forth, and to encompass all structural and functionalequivalents.

Relational terms such as “first” and “second” and the like may be usedsolely to distinguish one entity or action from another, withoutnecessarily requiring or implying any actual relationship or orderbetween them. The terms “comprises,” “comprising,” and any othervariation thereof when used in connection with a list of elements in thespecification or claims are intended to indicate that the list is notexclusive and that other elements may be included. Similarly, an elementproceeded by an “a” or an “an” does not, without further constraints,preclude the existence of additional elements of the identical type. Theabstract is provided to help the reader quickly ascertain the nature ofthe technical disclosure. It is submitted with the understanding that itwill not be used to interpret or limit the scope or meaning of theclaims. In addition, various features in the foregoing detaileddescription are grouped together in various embodiments to streamlinethe disclosure. This method of disclosure should not be interpreted asrequiring claimed embodiments to require more features than areexpressly recited in each claim. Rather, as the following claimsreflect, inventive subject matter lies in less than all features of asingle disclosed embodiment. Thus, the following claims are herebyincorporated into the detailed description, with each claim standing onits own as separately claimed subject matter.

What is claimed is:
 1. A Global Navigation Satellite System (GNSS)processing architecture, the GNSS processing architecture comprising: aprocessor; and a memory unit in communication with the processor via acommunication infrastructure and configured to store processor-readableinstructions; wherein, when executed by the processor, theprocessor-readable instructions cause the processor to: receive signaldata including one or more input metric values for one or more types ofinput parameters; provide the signal data to a neural network forclassification of the signal data; and classify, using the neuralnetwork, the signal data as being of a particular type of interferenceenvironment of a plurality of different types of interferenceenvironments.
 2. The GNSS processing architecture of claim 1, whereinthe plurality of different types of interference environments includetwo or more of (1) an interference-free environment, (2) an in-bandinterference environment, and (3) an out-of-band interferenceenvironment.
 3. The GNSS processing architecture of claim 1, wherein theneural network comprises one or more of an artificial neural network(ANN) and a convolutional neural network (CNN).
 4. The GNSS processingarchitecture of claim 1, wherein the one or more types of inputparameters is one or more of (1) a power spectral density parameter, (2)a carrier-to-noise ratio parameter, (3) a pseudo-noise tracking errorparameter, or (4) an estimated positioning error parameter.
 5. The GNSSprocessing architecture of claim 1, the processor-readable instructionsfurther cause the processor to estimate, after the classification of thesignal data as being of the particular type of interference environment,a value for one or more additional types of input parameters.
 6. TheGNSS processing architecture of claim 5, wherein the one or moreadditional types of input parameters include one or more of (1) afrequency center parameter, (2) a bandwidth parameter, or (3) a powerparameter.
 7. The GNSS processing architecture of claim 6, wherein theprocessor-readable instructions further cause the processor to: selectan interference mitigation technique assigned to the classification ofthe signal data as being of the particular type of interferenceenvironment and based on values for the one or more additional types ofinput parameters; and apply the selected interference mitigationtechnique.
 8. The GNSS processing architecture of claim 1, wherein theprocessor-readable instructions further cause the processor to: selectan interference mitigation technique assigned to the classification ofthe signal data as being of the particular type of interferenceenvironment; and apply the selected interference mitigation technique.9. A method comprising: receiving, at a Global Navigation SatelliteSystem (GNSS) processing architecture, signal data including one or moreinput metric values for one or more types of input parameters of aplurality of different input parameters, wherein the signal data is fromone or more received GNSS signals; providing the signal data to a neuralnetwork for classification of the signal data; and classifying, usingthe neural network, the signal data as being of a particular type ofinterference environment of a plurality of different types ofinterference environments.
 10. The method of claim 9, wherein theplurality of different types of interference environments include two ormore of (1) an interference-free environment, (2) an in-bandinterference environment, and (3) an out-of-band interferenceenvironment.
 11. The method of claim 9, wherein the neural networkcomprises one or more of an artificial neural network (ANN) and aconvolutional neural network (CNN).
 12. The method of claim 9, whereinthe plurality of different input parameters includes at least two of apower spectral density parameter, a carrier-to-noise ratio parameter, apseudo-noise tracking error parameter, or an estimated positioning errorparameter.
 13. The method of claim 9, further comprising estimating,after the classification of the signal data as being of the particulartype of interference environment, a value for one or more additionaltypes of input parameters.
 14. The method of claim 13, wherein the oneor more additional types of input parameters include one or more of (1)a frequency center parameter, (2) a bandwidth parameter, or (3) a powerparameter.
 15. The method of claim 14, further comprising: selecting aninterference mitigation technique assigned to the classification of thesignal data as being of the particular type of interference environmentand based on values for the one or more additional types of inputparameters; and applying the selected interference mitigation technique.16. The method of claim 9, further comprising: selecting an interferencemitigation technique assigned to the classification of the signal dataas being of the particular type of interference environment; andapplying the selected interference mitigation technique.
 17. A GlobalNavigation Satellite System (GNSS) receiver configured to: receivesignal data including one or more input metric values for one or moretypes of input parameters of a plurality of different types of inputparameters, wherein the signal data is from one or more GNSS signals;provide the signal data to a neural network for classification of thesignal data; and classify, using the neural network, the signal data asbeing of a particular type of environment of a plurality of differenttypes of environments, wherein the classification is used to identify amitigation technique of a plurality of different mitigation techniques.18. The GNSS receiver of claim 17, wherein the neural network comprisesone or more of an artificial neural network (ANN) and a convolutionalneural network (CNN).
 19. The GNSS receiver of claim 17, wherein theplurality of different input parameters includes at least two of a powerspectral density parameter, a carrier-to-noise ratio parameter, apseudo-noise tracking error parameter, or an estimated positioning errorparameter.
 20. The GNSS receiver of claim 17, further configured toestimate, after the classification of the signal data as being of theparticular type of environment, a value for one or more additional typesof input parameters of the plurality of different types of inputparameters.